--- title: "FineTuningLLMs vs peft" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dvgodoy-finetuningllms-vs-huggingface-peft" tools: ["dvgodoy-finetuningllms", "huggingface-peft"] --- # FineTuningLLMs vs peft *GraphCanon updated Aug 24, 2026* ## Verdict Pick FineTuningLLMs if fineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks; pick peft if pEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python. [FineTuningLLMs](https://github.com/dvgodoy/FineTuningLLMs) reports 855 GitHub stars, 116 forks, and 4 open issues, last pushed Feb 28, 2026. [peft](https://huggingface.co/docs/peft) has 22k stars, 2.4k forks, and 74 open issues, last pushed Aug 22, 2026. Figures are from public GitHub metadata via [FineTuningLLMs's repository](https://github.com/dvgodoy/FineTuningLLMs) and [peft's repository](https://github.com/huggingface/peft). | | [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) | [peft](/tools/huggingface-peft.md) | | --- | --- | --- | | Tagline | Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face' | State-of-the-art Parameter-Efficient Fine-Tuning | | Stars | 855 | 21,585 | | Forks | 116 | 2,446 | | Open issues | 4 | 74 | | Language | Jupyter Notebook | Python | | Adopt for | FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks. | PEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) | [peft](/tools/huggingface-peft.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 176d | 1d | | Open issues (now) | 4 | 74 | | Stars delta | +4 (30d) | +142 (30d) | | Open issues delta | 0 (30d) | +16 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/dvgodoy-finetuningllms/trust.md) | [trust report](/tools/huggingface-peft/trust.md) | ## Decision facts: FineTuningLLMs - **Adopt for:** FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks. ## Decision facts: peft - **Adopt for:** PEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python. ## Choose when ### Choose FineTuningLLMs if… - FineTuningLLMs is primarily Jupyter Notebook; peft is Python. - License: FineTuningLLMs is MIT, peft is Apache-2.0. - Tags unique to FineTuningLLMs: bitsandbytes, finetuning, hugging-face, large language models. - You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem ### Choose peft if… - peft is primarily Python; FineTuningLLMs is Jupyter Notebook. - License: peft is Apache-2.0, FineTuningLLMs is MIT. - Tags unique to peft: adapter, diffusion, llm, parameter-efficient-learning. - When you need to fine-tune large language models but are constrained by compute resources or want to avoid overfitting. ## When NOT to use FineTuningLLMs - Not interested in PyTorch; prefer TensorFlow or another framework - Seek theoretical background over practical applications ## When NOT to use peft - If you require a tool that supports training from scratch, as PEFT is specifically designed for fine-tuning purposes only. - When working on models where the full fine-tuning of all parameters is feasible or preferred due to ample compute resources and no concern over overfitting. ## Common questions ### What is the difference between FineTuningLLMs and peft? FineTuningLLMs: Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'. peft: State-of-the-art Parameter-Efficient Fine-Tuning. See the comparison table for live GitHub stats and shared categories. ### When should I choose FineTuningLLMs over peft? Choose FineTuningLLMs over peft when FineTuningLLMs is primarily Jupyter Notebook; peft is Python; License: FineTuningLLMs is MIT, peft is Apache-2.0; Tags unique to FineTuningLLMs: bitsandbytes, finetuning, hugging-face, large language models; You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem. ### When should I choose peft over FineTuningLLMs? Choose peft over FineTuningLLMs when peft is primarily Python; FineTuningLLMs is Jupyter Notebook; License: peft is Apache-2.0, FineTuningLLMs is MIT; Tags unique to peft: adapter, diffusion, llm, parameter-efficient-learning; When you need to fine-tune large language models but are constrained by compute resources or want to avoid overfitting. ### When should I avoid FineTuningLLMs? Not interested in PyTorch; prefer TensorFlow or another framework Seek theoretical background over practical applications ### When should I avoid peft? If you require a tool that supports training from scratch, as PEFT is specifically designed for fine-tuning purposes only. When working on models where the full fine-tuning of all parameters is feasible or preferred due to ample compute resources and no concern over overfitting. ### Is FineTuningLLMs or peft more popular on GitHub? peft has more GitHub stars (21,585 vs 855). Stars measure visibility, not whether either tool fits your constraints. ### Are FineTuningLLMs and peft open source? Yes - both are open-source projects on GitHub (FineTuningLLMs: MIT, peft: Apache-2.0). ### Where can I find alternatives to FineTuningLLMs or peft? GraphCanon lists graph-backed alternatives at [FineTuningLLMs alternatives](/tools/dvgodoy-finetuningllms/alternatives) and [peft alternatives](/tools/huggingface-peft/alternatives) ([FineTuningLLMs markdown twin](/tools/dvgodoy-finetuningllms/alternatives.md), [peft markdown twin](/tools/huggingface-peft/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/dvgodoy-finetuningllms-vs-huggingface-peft.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FineTuningLLMs or peft? FineTuningLLMs: Slowing. peft: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for FineTuningLLMs and peft? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FineTuningLLMs trust report](/tools/dvgodoy-finetuningllms/trust); [peft trust report](/tools/huggingface-peft/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dvgodoy-finetuningllms`](/api/graphcanon/graph?tool=dvgodoy-finetuningllms) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_